public static Matrix plusInplace(Matrix mat, double etat) {
int nrows = mat.getNumRows();
int ncols = mat.getNumColumns();
for (int r = 0; r < nrows; r++) {
for (int c = 0; c < ncols; c++) {
mat.setElement(r, c, mat.getElement(r, c) + etat);
}
}
return mat;
}
public static double colSparcity(Matrix mat) {
double ncols = mat.getNumColumns();
double nsparse = 0;
for (int c = 0; c < ncols; c++) {
if (mat.getColumn(c).sum() == 0) {
nsparse++;
}
}
return nsparse / ncols;
}
public static double absSum(Matrix mat) {
double tot = 0;
int nrows = mat.getNumRows();
int ncols = mat.getNumColumns();
for (int r = 0; r < nrows; r++) {
for (int c = 0; c < ncols; c++) {
tot += Math.abs(mat.getElement(r, c));
}
}
return tot;
}
public static Matrix abs(Matrix mat) {
Matrix ret = mat.clone();
int nrows = ret.getNumRows();
int ncols = ret.getNumColumns();
for (int r = 0; r < nrows; r++) {
for (int c = 0; c < ncols; c++) {
ret.setElement(r, c, Math.abs(mat.getElement(r, c)));
}
}
return ret;
}
@Test
public void testMatlabFile() throws IOException {
MatlabFileDataGenerator gen = new MatlabFileDataGenerator(matfile);
int nusers = -1;
int nwords = -1;
int ntasks = -1;
for (int i = 0; i < gen.size(); i++) {
Pair<Matrix> XY = gen.generate();
Matrix X = XY.firstObject();
Matrix Y = XY.secondObject();
if (nusers == -1) {
nusers = X.getNumColumns();
nwords = X.getNumRows();
ntasks = Y.getNumColumns();
} else {
assertTrue(nusers == X.getNumColumns());
assertTrue(nwords == X.getNumRows());
assertTrue(ntasks == Y.getNumColumns());
}
}
}
/**
* Creates a new {@code MatrixBasedTermSimilarityNetwork}.
*
* @param termIndex The index of terms that contains the nodes of the network.
* @param similarities The square matrix of similarities between terms. Must have a number of rows
* and columns equal to the number of terms in the term index.
*/
public MatrixBasedTermSimilarityNetwork(final TermIndex termIndex, final Matrix similarities) {
super();
if (similarities.getNumRows() != termIndex.getTermCount()
|| similarities.getNumColumns() != termIndex.getTermCount()) {
throw new DimensionalityMismatchException(
"the number of terms in the term index must match the "
+ "dimensions of the square similarities matrix");
}
this.setTermIndex(termIndex);
this.setSimilarities(similarities);
}
public static Vector diag(Matrix mat) {
Vector ret;
if (mat.getNumColumns() > mat.getNumRows()) {
ret = mat.getRow(0);
} else {
ret = mat.getColumn(0);
}
int rowcol = ret.getDimensionality();
for (int rc = 0; rc < rowcol; rc++) {
ret.setElement(rc, mat.getElement(rc, rc));
}
return ret;
}
public static Matrix vstack(MatrixFactory<? extends Matrix> matrixFactory, Matrix... matricies) {
int nrows = 0;
int ncols = 0;
for (Matrix matrix : matricies) {
nrows += matrix.getNumRows();
ncols = matrix.getNumColumns();
}
Matrix ret = matrixFactory.createMatrix(nrows, ncols);
int currentRow = 0;
for (Matrix matrix : matricies) {
ret.setSubMatrix(currentRow, 0, matrix);
currentRow += matrix.getNumRows();
}
return ret;
}
public double sumLoss(
List<Pair<Matrix>> pairs, Matrix u, Matrix w, Matrix bias, BilinearLearnerParameters params) {
LossFunction loss = params.getTyped(BilinearLearnerParameters.LOSS);
loss = new MatLossFunction(loss);
double total = 0;
int i = 0;
int ntasks = 0;
for (Pair<Matrix> pair : pairs) {
Matrix X = pair.firstObject();
Matrix Y = pair.secondObject();
SparseMatrix Yexp = BilinearSparseOnlineLearner.expandY(Y);
Matrix expectedAll = u.transpose().times(X.transpose()).times(w);
loss.setY(Yexp);
loss.setX(expectedAll);
if (bias != null) loss.setBias(bias);
logger.debug("Testing pair: " + i);
total += loss.eval(null); // Assums an identity w.
i++;
ntasks += Y.getNumColumns();
}
total /= ntasks;
return Math.sqrt(total);
}
// Note this method isn't static, because we want to override to make
// it parallelized --krdixon
protected void normalizeTransitionMatrix(Matrix A) {
final int k = A.getNumColumns();
for (int j = 0; j < k; j++) {
normalizeTransitionMatrix(A, j);
}
}